Tag Archives: Engineering

A Mickey Mouse-shaped solar farm

Fig. 1: An aerial view of the Mickey Mouse-shaped solar farm
Fig. 2: An Energy3D model of the Mickey Mouse-shaped solar farm
If I didn't tell you that this is an actual solar farm near the Epcot Theme Park in the Disney World in Orlando, Florida, you probably would think this is some kind of school project done by kids. But no, this 22-acre 5 MW project was designed and installed by Duke Energy and it has been powering Disney World's facilities since 2016 (Figure 1 is an image from Disney.com). So this is some kind of serious business and has drawn a lot of media attention. The solar farm is so new that even the latest version of Google Maps in May 2017 still does not show it (it is available through Google Maps API that we are using, though).

By shaping the beloved Mickey Mouse character with tens of thousands of solar panels, Disney World has delivered a strong message to the world that the company is committed to a sustainable future.

Fig. 3: A solar radiation heat map representation (June 22).
But who says that kids should not do this? Perhaps they couldn't do it because of the lack of appropriate support and tool. Not any more. Thanks to the support from the National Science Foundation, our powerful Energy3D software and our Solarize Your World curriculum can probably turn every wild imagination in solar power into virtual reality, particularly for children who may need more inquiry- and design-based activities that connect so deeply to their world and their future. Figure 2 shows a model of the Mickey Mouse-shaped solar farm in Energy3D and Figure 3 shows a heat map representation of the solar radiation onto the solar panel arrays.

Designing ground-mounted solar panel arrays: Part III

Fig. 1: Rows of solar panels on racks in a solar farm
The most common configuration of solar farms is perhaps arrays consisting of rows of solar panel racks such as shown in Figure 1. But have you ever thought about why? Can we challenge this conventional wisdom?

Fig.2: Cover the field with horizontally-placed solar panels
Obviously, some inter-row spacing allows for easier cleaning and maintenance and, perhaps, even integration with agricultural farming (e.g., growing mushrooms that prefer shaded areas). But let's put those benefits aside for now and just consider the energy part of the problem. Let me point out a fact: If we completely cover the entire field with solar panels with zero tilt angle and zero gap (Figure 2), we are guaranteed to capture almost every single photon that strikes the area regardless of time and location. Such a simple-minded "design" will produce the maximal output of any given field at any location and time and there is absolutely no such problem as inter-row shading. So what solar design?
Fig. 3: Comparing two hypothetical fields.

It turns out that, although the simple-minded design can surely generate maximum electricity, each individual solar panel in it does not necessarily generate a maximum amount of electricity over the course of a year, compared with other designs. In other words, it may just use more solar panels to generate more electricity. As engineering design must consider cost effectiveness and even put it as a top priority, an engineer's job is then to look for a better solution that maximizes the production of each solar panel.

Fig. 4: Compare outputs of single panels in two fields (Boston).
A great advantage of Energy3D is that it allows one to experiment with ideas rapidly. So let's create a field with tilted rows of solar panels and leave some gap between them and then use the Group Analysis Tools to compare the daily and annual outputs of individual solar panels in the two hypothetical fields (Figure 3). And let's assume the fields are in Boston.

Fig. 5: Compare outputs of single panels in two fields (Phoenix).
Figure 4 shows that the total annual output of a single solar panel in the field of tilted rows is nearly 20% higher than that of a single solar panel in the field of flat cover in Boston (42° N). In this simulation, the tilt angle was set to be equal to the latitude. This cost effectiveness is one of the main reasons why we choose tilted rows of solar panels in high-latitude areas (aside from the fact that tilted angles allow rain to wash panels more efficiently and snow to slide from them more quickly).

Caveat for low-latitude locations


Fig. 6: Compare outputs of single panels in two fields (Mexico).
Note that this result applies only to high-latitude areas such as Boston. If we are designing solar farms for tropical areas such as Singapore, the story may be completely different. In low-latitude areas, small or even zero tilt angles make sense. Therefore, the design principle may be to cover the field with as many solar panels as possible or to use trackers to increase individual outputs (whichever is more economic depends on the relative prices of solar panels and solar trackers that change all the time). You can experiment with Energy3D to find out at which latitude this principle starts to become dominant. Figure 5 shows that the results in cities with a lower latitude such as Phoenix (33° N) and Mexico City (19° N) in North America. In the case of Phoenix, AZ, the gain from the tilted rows drops to about 10%. In the case of Mexico City, it drops to 5%. So designing a ground-mounted solar array for Mexico may be very different from designing a ground-mounted solar array for Canada.

A complete 3D model of the PS20 solar power plant

According to Wikipedia, the 20 MW PS20 Solar Power Plant in Seville, Spain consists of a solar field of 1,255 heliostats. Each heliostat, with a surface area of 120 square meters(!), automatically tracks the sun on two axes and reflects the solar radiation it receives onto the central receiver, located at the top of a tower that is as tall as 165 meters. The concentrated heat vaporizes water and produces steam that drives a turbine to generate electricity. The Wikipedia page mentions that PS20 uses a thermal storage system, but it is not clear whether it is a molten salt tank or not.

PS20 generates about 48,000 MWh per year, or roughly 132 MWh per day on average without considering seasonal variations.

The full 3D model of the PS20 plant is now available in Energy3D and can be downloaded from http://energy.concord.org/energy3d/designs/ps20-solar-tower.ng3. While it generally costs hundreds of millions of dollars to design and build such a futuristic power plant, it costs absolutely nothing to do so in the virtual space of Energy3D. In a way, Energy3D gives everyone, especially those in developing nations, a powerful tool to explore the solar potential of their regions. Whether you live in a desert or on the coast, near or far away from the equator, in cities or rural areas, you can imagine all sorts of possibilities with it.

I am working on heat transfer, energy conversion, and thermal storage models that can predict the electricity generation accurately. Right now, Energy3D estimates the raw solar radiation input to the receiver on June 22 to be about 656 MWh, considering all the shadowing and blocking losses. If the system efficiency of heat transfer and energy conversion is in the range of 30-50%, then Energy3D's prediction will fall into a reasonable range.

Designing ground-mounted solar panel arrays: Part II

To design a solar panel array, we need to understand the specifications of the type of solar panel that we are going to use (here is an example of the specs of SunPower's X21-series). Although all solar panels provide nominal maximum power outputs (Pmax or Pnom), those numbers specify the DC power outputs under the Standard Test Conditions (STC) or PVUSA Test Conditions (PTC). Those numbers only provide some standardized values for customers' reference and cannot be used to calculate the electricity generation in the real world. Although each brand of solar panel may be designed in different ways and the specs vary, there are a few scientific principles that govern most of them. The calculation of power generation can therefore be drawn upon these fundamental principles. This article covers some of these principles.

The first parameter for solar power calculation is the solar cell efficiency, which defines the percentage of incident sunlight that can be converted into electricity by a cell of the solar panel. This property is usually determined by the semiconductor materials used to make the cell. Monocrystalline silicon-based materials tend to have a higher efficiency than polycrystalline ones. As of 2017, the solar cell efficiency for most solar panels in the market typically ranges from 15% to 25%. The higher the efficiency, the more expensive the solar panel.

Figure 1: All cells in a series (left) and diode bypasses (right)
The solar cell efficiency generally decreases when the temperature increases. To reflect this relationship, solar panels usually specify the Nominal Operating Cell Temperature (NOCT) and the Temperature Coefficient of Pmax. The former describes how high the temperature of the cell rises to under the sun. The latter describes how much the solar cell efficiency drops as the cell temperature rises. If we know the solar cell efficiency under STC, the NOCT, the Temperature Coefficient of Pmax, the air temperature, and the solar radiation density on the surface of the cell, we can compute the actual efficiency of the solar cell at current time.

Now, in order to compute the actual power output of the cell, we will need to know two more things: the area of the cell and the angle between the surface of the cell and the direction of the sun. The area of the cell is related to the packing density of the cells on a solar panel. Polycrystalline solar cells can have nearly 100% of packing density as they are usually rectangular, whereas monocrystalline ones have less packing density as they usually have round corners (therefore, they can't use up the entire surface area of a solar panel). The angle between the cell and the sun depends on how the solar panel is installed. This usually comes down to its tilt angle and azimuth.

Figure 2: Landscape vs. portrait (diode bypasses, location: Boston)
All these parameters are needed in Energy3D's solar radiation simulation. As a user, what you have to do is to understand the meaning of these parameters while designing your solutions and set the parameters correctly for your simulations. As Energy3D hasn't provided a way to select a solar panel model and then automatically import all of its specs, you still have to define a solar panel brand by setting its properties manually.

The next thing we must consider is a little tricky. A solar panel is made of many cells, arranged in an array of, for example 6 × 10. In order for the cells to produce usable voltage, they are usually connected in a series (the left image in Figure 1). In this case, the electric current flowing through each cell is the same but the voltage adds up. However, the problem with a series circuit is that, if one cell gets shaded by, say, a leaf that falls on it, and as a result generates a weaker current, every other cell of the panel will end up generating a smaller output (worse, all the generated electricity that cannot flow freely will turn into heat and damage the cells). To mitigate this problem, most solar panels today use diode bypasses (the right image in Figure 1) or similar technologies to allow the part of the solar panel that is not shaded to be able to contribute to the overall output. However, if the shade is not as spotty as is in the case of a leaf, even the diode bypasses will not be able to prevent complete loss (this video nicely demonstrates the problem). Therefore, our design of solar arrays must consider the actual wiring of the solar cells on the solar panel that we choose.

Figure 3: Month-by-month outputs of four arrays in Figure 2.
What are the implications of the cell wiring? Figure 2 shows four solar panel arrays with two different inter-row distances but the same number of identical solar panels that connect their cells with diode bypasses. The size of each solar panel is about 1 meter × 2 meters. On the racks of two arrays, the solar panels are placed in the landscape orientation -- each rack has therefore four rows of solar panels. On the racks of the other two arrays, they are placed in the portrait orientation -- each rack has therefore two rows of solar panels. When the inter-row spacing between two adjacent racks is the same, our simulation suggests that the landscape array always generates more electricity than the portrait array. This difference demonstrates the effect of the cell wiring using diode bypasses. In the front part of Figure 2 for arrays with narrower inter-row spacing, the simulation shows that about a quarter of the area on the racks after the first one is shaded during the course of the day (as indicated by their blue coloring). When the solar panels at the bottom of a rack is shaded, a portrait orientation reduces the output of 50% of the solar panels (there are two rows of solar panels on each rack in the portrait array shown in Figure 2), while a landscape orientation reduces the output of 25% of the solar panels (there are four rows of solar panels on each rack in the landscape array shown in Figure 2). The difference becomes less when the inter-row distance is longer. So when you have a limited space to place your solar arrays, you should probably favor the landscape orientation.

Figure 4: Shadow analysis shows inter-row shading in four seasons.
Of course, the output of a solar array depends also on the season. When the sun is high in the sky in the summer, the inter-row shading becomes less a problem. It is during the winter months when the shading loss becomes significant. This is shown in Figure 3. A snapshot of the shadow analysis (Figure 4) illustrates the difference visually.

For sites in the snowy north, another factor in the winter that favors the landscape orientation is the effect of snow accumulation on the panels. As soon as snow slides off the upper third of a solar panel in the landscape arrangement, it will start to generate some electricity. In the case of the portrait arrangement, it has to wait until all the snow comes off the panel.

Note that this article is concerned only with the cell wiring on a solar panel. The wiring of solar panels in an array is another important topic that we will cover later.

Introducing the Virtual Solar Decathlon

Hypothetical solar power near Hancock Tower in Boston
At the ACE Hackathon event on April 28, we introduced the concept of the Virtual Solar Decathlon to students at Phillips Academy who are interested in sustainable development.

Hypothetical solar canopies at Andover High School
The U.S. Department of Energy's Solar Decathlon challenges 20 collegiate teams to design, build, and operate solar-powered houses that are forward-thinking and cost-effective. Such a project, however, may take up to a year to complete and cost up to $250,000.

PS20 solar power tower in Seville, Spain
For a few years, I have been thinking about creating a high school equivalent of the Solar Decathlon that costs nothing, takes a much shorter time, and allows everyone to participate. The result of this thinking process is the Virtual Solar Decathlon that can now be supported by our Energy3D CAD software (and increasingly so as we added new features to allow more clean energy technologies to be simulated and designed). The goal of the Virtual Solar Decathlon is to turn the entire Google Earth into a simulation-based engineering lab of renewable energy and engage students to change their world by tackling energy problems (at least virtually) that matter deeply to their lives.

Here is the link to our presentation at Phillips Academy.

High school students to solarize the city of Lowell — virtually


In April, high school students in Lowell, Massachusetts will start exploring various solarization possibilities in the city of Lowell -- famously known as the Cradle of American Industrial Revolution. Many municipal properties and apartment buildings in Lowell have large roofs that are ideal for rooftop solar installations. Public parking facilities also provide space for installing solar canopies, which serve the dual purpose of generating clean energy and providing shade for parked cars. Students will discover the solar potential of their city and calculate the amount of electricity that can generated based on it.

This project is made possible by our Energy3D software, which supports engineering-grade solar design, simulation, and analysis. The Lowell High School, local business owners, and town officials have been very supportive about this initiative. They provided a number of public and private sites for students to pick and choose. Some of them have even agreed to serve as the "clients" for students to provide specifications, inputs, and feedback to students while they are carrying out this engineering project.

Among the available sites, five public parking garages managed by the municipal authority, which have not installed solar canopies, will be investigated by students through feasibility studies that include 3D modeling, solar energy simulation, and financial planning. Through the project work, students will author reports addressed to the property owners, in which they will recommend appropriate solar solutions and financial options.

Solving real-world problems like these creates a meaningful and compelling context and pathway for students to learn science and engineering knowledge and skills. Hopefully, their work will also help inform the general public about the solar potential of their city and the possibility of transitioning it to 100% renewable energy in the foreseeable future, which is a goal recently set by Massachusetts lawmakers.

Simulating photovoltaic power plants with Energy3D

Modeling 1,000 PV panels in a desert
Solar radiation simulation
We have just added new modeling capacities to our Energy3D software for simulating photovoltaic (PV) power stations. With these additions, the latest version of the software can now simulate rooftop solar panels, solar parks, and solar power plants. Our plan is to develop Energy3D into a "one stop shop" for solar simulations. The goal is to provide students an accessible (yet powerful) tool to learn science and engineering in the context of renewable energy and professionals an easy-to-use (yet accurate) tool to design, predict, and optimize renewable energy generation.

Users can easily copy and paste solar panels to create an array and then duplicate arrays to create more arrays. In this way, users can rapidly add many solar panels. Each solar panel can be rotated around three different axes (normal, zenith, and azimuth). With this flexibility, users can create a PV array in any direction and orientation. At any time, they can adjust the direction and orientation of any or all solar panels.
PV arrays that are oriented differently


What is in the design of a solar power plant? While the orientation is a no-brainer, the layout may need some thinking and planning, especially for a site that has a limited area. Another factor that affects the layout is the design of the solar tracking system used to maximize the output. Also, considering that many utility companies offer peak and off-peak prices for electricity, users may explore strategies of orienting some PV arrays towards the west or southwest for the solar power plant to produce more energy in the afternoon when the demand is high in the summer, especially in the south.

Rooftop PV arrays
In addition to designing PV arrays on the ground, users can do the same thing for flat rooftops as well. Unlike solar panels on pitched roofs of residential buildings, those on flat roofs of large buildings are usually tilted.

We are currently implementing solar trackers so that users can design solar power plants that maximize their outputs based on tracking the sun. Meanwhile, mirror reflector arrays will be added to support the design of concentrated solar power plants. These features should be available soon. Stay tuned!

Energy3D makes designing realistic buildings easy

The annual yield and cost benefit analyses of rooftop solar panels based on sound scientific and engineering principles are critical steps to the financial success of building solarization. Google's Project Sunroof provides a way for millions of property owners to get recommendations for the right solar solutions.



Another way to conduct accurate scientific analysis of solar panel outputs based on their layout on the rooftop is to use a computer-aided engineering (CAE) tool to do a three-dimensional, full-year analysis based on ab initio scientific simulation. Under the support of the National Science Foundation since 2010, we have been developing Energy3D, a piece of CAE software that has the goal of bringing the power of sophisticated scientific and engineering simulations to children and laypersons. To achieve this goal, a key step is to support users to rapidly sketch up their own buildings and the surrounding objects that may affect their solar potentials. We feel that most CAD tools out there are probably too difficult for average users to create realistic models of their own houses. This forces us to invent new solutions.

We have recently added countless new features to Energy3D to progress towards this goal. The latest version allows many common architectural styles found in most parts of the US to be created and their solar potential to be studied. The screenshots embedded in this article demonstrate this capability. With the current version, each of these designs took myself approximately an hour to create from scratch. But we will continue to push the limit.

The 3D construction user interface has been developed based on the tenet of supporting users to create any structure using a minimum set of building blocks and operations. Once users master a relatively small set of rules, they are empowered to create almost any shape of building as they wish.

Solar yield analysis of the first house
The actual time-consuming part is to get the right dimension and orientation of a real building and the surrounding tall objects such as trees.
Google's 3D map may provide a way to extract these data. Once the approximate geometry of a building is determined, users can easily put solar panels anywhere on the roof to check out their energy yield. They can then try as many different layouts as they wish to compare the yields and select an optimal layout. This is especially important for buildings that may have partial shades and sub-optimal orientations. CAE tools such as Energy3D can be used to do spatial and temporal analysis and report daily outputs of each panel in the array, allowing users to obtain fine-grained, detailed results and thus providing a good simulation of solar panels in day-to-day operation.

The engineering principles behind this solar design, assessment, and optimization process based on science is exactly what the Next Generation Science Standards require K-12 students in the US to learn and practice. So why not ask children for help to solarize their own homes, schools, and communities, at least virtually? The time for doing this can never be better. And we have paved the road for this vision by creating one of easiest 3D interfaces with compelling scientific visualizations that can potentially entice and engage a lot of students. It is time for us to test the idea.

To see more designs, visit this page.

The National Science Foundation funds SmartCAD—an intelligent learning system for engineering design

We are pleased to announce that the National Science Foundation has awarded the Concord Consortium, Purdue University, and the University of Virginia a $3 million, four-year collaborative project to conduct research and development on SmartCAD, an intelligent learning system that informs engineering design of students with automatic feedback generated using computational analysis of their work.

Engineering design is one of the most complex learning processes because it builds on top of multiple layers of inquiry, involves creating products that meet multiple criteria and constraints, and requires the orchestration of mathematical thinking, scientific reasoning, systems thinking, and sometimes, computational thinking. Teaching and learning engineering design becomes important as it is now officially part of the Next Generation Science Standards in the United States. These new standards mandate every student to learn and practice engineering design in every science subject at every level of K-12 education.
Figure 1

In typical engineering projects, students are challenged to construct an artifact that performs specified functions under constraints. What makes engineering design different from other design practices such as art design is that engineering design must be guided by scientific principles and the end products must operate predictably based on science. A common problem observed in students' engineering design activities is that their design work is insufficiently informed by science, resulting in the reduction of engineering design to drawing or crafting. To circumvent this problem, engineering design curricula often encourage students to learn or review the related science concepts and practices before they try to put the design elements together to construct a product. After students create a prototype, they then test and evaluate it using the governing scientific principles, which, in turn, gives them a chance to deepen their understanding of the scientific principles. This common approach of learning is illustrated in the upper image of Figure 1.

There is a problem in the common approach, however. Exploring the form-function relationship is a critical inquiry step to understanding the underlying science. To determine whether a change of form can result in a desired function, students have to build and test a physical prototype or rely on the opinions of an instructor. This creates a delay in getting feedback at the most critical stage of the learning process, slowing down the iterative cycle of design and cutting short the exploration in the design space. As a result of this delay, experimenting and evaluating "micro ideas"--very small stepwise ideas such as those that investigate a design parameter at a time--through building, revising, and testing physical prototypes becomes impractical in many cases. From the perspective of learning, however, it is often at this level of granularity that foundational science and engineering design ultimately meet.

Figure 2
All these problems can be addressed by supporting engineering design with a computer-aided design (CAD) platform that embeds powerful science simulations to provide formative feedback to students in a timely manner. Simulations based on solving fundamental equations in science such as Newton’s Laws model the real world accurately and connect many science concepts coherently. Such simulations can computationally generate objective feedback about a design, allowing students to rapidly test a design idea on a scientific basis. Such simulations also allow the connections between design elements and science concepts to be explicitly established through fine-grained feedback, supporting students to make informed design decisions for each design element one at a time, as illustrated by the lower image of Figure 1. These scientific simulations give the CAD software tremendous disciplinary intelligence and instructional power, transforming it into a SmartCAD system that is capable of guiding student design towards a more scientific end.

Despite these advantages, there are very few developmentally appropriate CAD software available to K-12 students—most CAD software used in industry not only are science “black boxes” to students, but also require a cumbersome tool chaining of pre-processors, solvers, and post-processors, making them extremely challenging to use in secondary education. The SmartCAD project will fill in this gap with key educational features centered on guiding student design with feedback composed from simulations. For example, science simulations can be used to analyze student design artifacts and compute their distances to specific goals to detect whether students are zeroing in towards those goals or going astray. The development of these features will also draw upon decades of research on formative assessments of complex learning.